1. Field of the Invention
This invention relates generally to color mixing and, in particular, to predictive color mixing models.
2. Description of the Related Art
It is generally desirable to determine the mix of primary colors necessary to obtain another color. This is particularly true when mixing primary colors that deviate from the ideal or theoretical characteristics for those color. In other words, the issues is particularly relevant when mixing actual colors, as actual color inks have spectral absorption curves that deviate from theoretical absorption curves. The primary colors may be red (R), green (G), and blue (B), (which may collectively be referred to as RGB), as is generally used in color additive system, or cyan (C), magenta (M), and yellow (Y), (which may collectively be referred to as CMY), as is generally used in color subtractive systems. RGB may herein be referred to as primary additive colors. Similarly, CMY may herein be referred to as primary subtractive colors. Alternatively, other colors may be used as primary colors which are used as a base for obtaining other colors.
One predictive color model is the Neugebauer equation. However, the Neugebauer equation is quite complex involving eight variables, which account for the primary additive colors RGB, the primary subtractive colors CMY, as well as black ink and white paper. Due to its complexity, the Neugebauer equation is difficult to apply.
Another predictive model relies on empirical data. In this model, color patches for different colors are printed using a device that is to be characterized. Thereafter, the color values for the patches are measured using a color measuring device, such as a spectrophotometer. Then the data is plotted on a color space. Using the plot on a color space, the necessary mixture for a desired color can be determined. The disadvantages of this model include the following. First, it is time consuming as it involves printing many different combinations of color patches. Second, it is primarily empirical in nature. As a result, its accuracy depends on the number of samples used and increases with an increasing number of samples. However, the larger number of samples increases the time and work required for creating the model. Third, it is not easily transferable to a new device. Anytime a new device is characterized for color combination, the entire process outlined above is repeated as different devices generally have different color characteristics.
It is also generally desirable to obtain a good gray balance or neutral hue when mixing colors. As in the general color mixing context, conventional techniques for obtaining gray balance generally involve empirical methods of trial and error.
The present invention is intended to address this and other disadvantages of conventional predictive color mixing models.